关键词: 多标签学习, 局部标签相关性, 标签特有特征, 相关实例补充

Abstract: Aiming at the effective utilization of label specific features and label correlation, a new multi-label algorithm LSFLC is proposed, which can effectively integrate label specific features and label correlation. Firstly, for each label, new positive class instances are generated by re-sampling technology to expand the number of positive class instances. Secondly, the original feature space is transformed into a specific feature space by feature mapping function, and the label specific feature set of each label is obtained. Then, for each label, its most relevant label is found to expand its specific feature set by copying the positive class instances of the label, which not only enriches the information of the label, but also solves the problem of class imbalance to a certain extent. Finally, experiments on different data sets show that the classification effect of the proposed algorithm is better.

Key words: multi-label learning, local label correlation, label-specific features, related instances insertion